Tool Models

from xpander_sdk import ToolCallType

# Tool call types
print(ToolCallType.XPANDER)  # Tools provided by the xpander.ai platform
print(ToolCallType.LOCAL)    # Tools implemented locally in your code
print(ToolCallType.UNKNOWN)  # Unrecognized tool type

from xpander_sdk import ToolCall, ToolCallType

# Create a tool call for a web search
web_search = ToolCall(
    name="web_search",
    type=ToolCallType.XPANDER,
    payload={
        "bodyParams": {
            "query": "latest AI research papers"
        }
    },
    tool_call_id="call_123456789"
)

# Access tool call properties
print(f"Tool name: {web_search.name}")
print(f"Tool type: {web_search.type}")
print(f"Tool payload: {web_search.payload}")
print(f"Tool call ID: {web_search.tool_call_id}")

from xpander_sdk import XpanderClient, LLMProvider
from openai import OpenAI

# Initialize OpenAI client
openai_client = OpenAI(api_key="your-openai-key")

# Get LLM response with tool calls
response = openai_client.chat.completions.create(
    model="gpt-4.1",
    messages=[{"role": "user", "content": "What's the weather in London?"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state or country"
                    }
                },
                "required": ["location"]
            }
        }
    }]
)

# Extract tool calls in xpander.ai format
tool_calls = agent.extract_tool_calls(
    llm_response=response.model_dump(),
)

# Process the tool calls
for tool_call in tool_calls:
    print(f"Tool: {tool_call.name}")
    print(f"Type: {tool_call.type}")
    print(f"Payload: {tool_call.payload}")

from xpander_sdk import ToolCallResult

# Create a successful tool call result
success_result = ToolCallResult(
    function_name="web_search",
    tool_call_id="call_123456789",
    is_success=True,
    result="Found the following information: OpenAI released GPT-4 Turbo on...",
    payload={"query": "latest AI research"}
)

# Create a failed tool call result
error_result = ToolCallResult(
    function_name="get_stock_price",
    tool_call_id="call_987654321",
    is_success=False,
    error="API rate limit exceeded",
    payload={"symbol": "AAPL"}
)

# Access properties
print(f"Tool name: {success_result.function_name}")
print(f"Success: {success_result.is_success}")
print(f"Result: {success_result.result}")

from xpander_sdk import XpanderClient, ToolCall, ToolCallType

# Initialize client and get agent
client = XpanderClient(api_key="your-api-key")
agent = client.agents.get(agent_id="agent-1234")

# Create a tool call
tool_call = ToolCall(
    name="web_search",
    type=ToolCallType.XPANDER,
    payload={
        "bodyParams": {
            "query": "latest advances in quantum computing"
        }
    },
    tool_call_id="call_1234"
)

# Execute the tool
result = agent.run_tool(tool=tool_call)

# Process the result
if result.is_success:
    print(f"Tool succeeded with result: {result.result[:100]}...")
else:
    print(f"Tool failed with error: {result.error}")

from xpander_sdk import LLMProvider

# Available LLM providers
print(LLMProvider.OPEN_AI)        # OpenAI (GPT models)
print(LLMProvider.FRIENDLI_AI)    # Claude (via FriendliAI)
print(LLMProvider.GEMINI_OPEN_AI) # Google Gemini (OpenAI-compatible)
print(LLMProvider.OLLAMA)         # Ollama (local models)

from xpander_sdk import XpanderClient, LLMProvider
from openai import OpenAI

# Initialize clients
xpander_client = XpanderClient(api_key="your-api-key")
openai_client = OpenAI(api_key="your-openai-key")
agent = xpander_client.agents.get(agent_id="agent-1234")

# Add task - this initializes memory and supports loading previous threads with thread_id
agent.add_task(
    input="What's the weather in Paris?",
    thread_id="optional-previous-thread-id"  # Optional: continue from previous conversation
)

# Get tools formatted for OpenAI

# Use the tools with OpenAI
response = openai_client.chat.completions.create(
    model="gpt-4.1",
    messages=agent.messages,
    tools=tools,
    tool_choice="auto"
)

# Extract tool calls from the response
tool_calls = agent.extract_tool_calls(
    llm_response=response.model_dump(),
)